GPT-6 Sol vs Muse Spark Open
Updated October 21, 2026. 25 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick GPT-6 Sol for coding: it scores 82.8 on the Vibe Coding Index against 62.1 for Muse Spark Open.
Pick Muse Spark Open when output tokens cost 2.4x less, or when what matters is SciCode or LiveBench research, where it leads.
- Shared results
- 25
- GPT-6 Sol only
- 75
- Muse Spark Open only
- 1
- Like-for-like categories
- 6 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, four public coding benchmarks
GPT-6 Sol
GPT-6 Sol scores 82.8 against 62.1.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
No call
Only GPT-6 Sol has a AA Intelligence Index result, 47.6.
Directional only
Web design
Design Arena: websites
Muse Spark Open
Muse Spark Open leads, 1311 against 1292.
Same board, both measured
Long documents
Listed context window
GPT-6 Sol
GPT-6 Sol takes 1.05M tokens against 1.05M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Muse Spark Open
GPT-6 Sol $0.0070, Muse Spark Open $0.0034 per turn.
List prices
At a glance
- Coding index
- GPT-6 Sol 82.8 from 2 benchmarks, Muse Spark Open 62.1 from 2 benchmarks.
- Widest category
- Agentic work, 23.4 points apart on average over 8 shared benchmarks.
- Widest single gap
- LiveBench browser: GPT-6 Sol 92.0% against Muse Spark Open 36.0%, 56.0 points apart on a 0 to 100 scale.
- Output price
- GPT-6 Sol $10.00 against Muse Spark Open $4.25 per million output tokens, 2.4x apart.
- Context window
- GPT-6 Sol takes 1.05M tokens, Muse Spark Open 1.05M.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | GPT-6 Sol | Bars | Muse Spark Open | Ties |
|---|---|---|---|---|
| Coding | 8 | 1 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 4 | 4 | ||
| Reasoning | 2 | 0 | ||
| Web design | 2 | 1 | ||
| General | 2 | 0 | ||
| All shared boards | 19 | 6 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index81
| Benchmark | GPT-6 Sol | Muse Spark Open | Leader |
|---|---|---|---|
| LiveBench coding | 93.1% | 73.6% | GPT-6 Sol |
| LMArena WebDev | 1683 | 1528 | GPT-6 Sol |
| Terminal-Bench 4 (Vals AI) | 44.4% | 6.6% | GPT-6 Sol |
| Vibe Code Bench (Vals AI) | 87.8% | 76.2% | GPT-6 Sol |
| Code migration (Vals AI) | 57.2% | 21.2% | GPT-6 Sol |
| ProgramBench (Vals AI) | 2.0% | Not measured | Only GPT-6 Sol measured |
| IOI (Vals AI) | 82.6% | Not measured | Only GPT-6 Sol measured |
| Terminal-Bench 2.1 (Vals AI) | 83.2% | Not measured | Only GPT-6 Sol measured |
| CyberBench (Vals AI) | 78.0% | Not measured | Only GPT-6 Sol measured |
| SciCode | 57.6% | 58.4% | Muse Spark Open |
| FrontierCode | 49.3% | Not measured | Only GPT-6 Sol measured |
| ALE-Bench | 2462.0 | 1590.1 | GPT-6 Sol |
| LMArena Text: coding | 1448 | Not measured | Only GPT-6 Sol measured |
| LMArena Text: software and IT | 1433 | Not measured | Only GPT-6 Sol measured |
| LMArena WebDev: React | 1685 | 1529 | GPT-6 Sol |
| LMArena WebDev: HTML | 1685 | 1495 | GPT-6 Sol |
| LMArena WebDev: from an image | 1660 | Not measured | Only GPT-6 Sol measured |
| Neither model has a result on the other 21 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-6 Sol | Muse Spark Open | Leader |
|---|---|---|---|
| AA Intelligence Index | 47.6 | Not measured | Only GPT-6 Sol measured |
| Epoch Capabilities Index | 163.7 | 155.1 | GPT-6 Sol |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index44
| Benchmark | GPT-6 Sol | Muse Spark Open | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1232 | Not measured | Only GPT-6 Sol measured |
| Design Arena: agents building full-stack apps | 1238 | Not measured | Only GPT-6 Sol measured |
| Design Arena: agents building mobile apps | 1216 | Not measured | Only GPT-6 Sol measured |
| LiveBench research | 69.3% | 80.3% | Muse Spark Open |
| LiveBench browser | 92.0% | 36.0% | GPT-6 Sol |
| LiveBench long agentic | 66.9% | 18.9% | GPT-6 Sol |
| SREBench (Vals AI) | 30.5% | Not measured | Only GPT-6 Sol measured |
| Finance Agent (Vals AI) | 49.0% | 61.3% | Muse Spark Open |
| Tax Agent Bench (Vals AI) | 15.0% | 26.3% | Muse Spark Open |
| Legal Research Bench (Vals AI) | 28.9% | 38.5% | Muse Spark Open |
| Excel Modeling Benchmark (Vals AI) | 71.5% | 53.7% | GPT-6 Sol |
| BioMysteryBench (Vals AI) | 74.8% | Not measured | Only GPT-6 Sol measured |
| Terminal-Bench-Science (Vals AI) | 30.0% | Not measured | Only GPT-6 Sol measured |
| Vals Science Index | 45.0% | Not measured | Only GPT-6 Sol measured |
| APEX-Agents | 54.3% | 33.4% | GPT-6 Sol |
| SpireClimb | 7.5% | Not measured | Only GPT-6 Sol measured |
| Vending-Bench 2 | $14,428 | Not measured | Only GPT-6 Sol measured |
| Neither model has a result on the other 22 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-6 Sol | Muse Spark Open | Leader |
|---|---|---|---|
| LiveBench reasoning | 88.7% | 73.7% | GPT-6 Sol |
| MysteryMechanism (Vals AI) | 30.2% | Not measured | Only GPT-6 Sol measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 89.0% | Only Muse Spark Open measured |
| Humanity's Last Exam (Artificial Analysis run) | 47.9% | 43.9% | GPT-6 Sol |
| GPQA Diamond (Epoch AI run) | 94.3% | Not measured | Only GPT-6 Sol measured |
| ARC-AGI | 95.5% | Not measured | Only GPT-6 Sol measured |
| ARC-AGI-2 | 89.6% | Not measured | Only GPT-6 Sol measured |
| Chess puzzles | 56.0% | Not measured | Only GPT-6 Sol measured |
| Mystery game puzzles | 56.0% | Not measured | Only GPT-6 Sol measured |
| EBR-bench | 53.3% | Not measured | Only GPT-6 Sol measured |
| LMArena Text: hard prompts | 1422 | Not measured | Only GPT-6 Sol measured |
| LMArena Text: expert | 1437 | Not measured | Only GPT-6 Sol measured |
| LMArena Text: hard prompts in English | 1420 | Not measured | Only GPT-6 Sol measured |
| LMArena Vision: diagrams | 1270 | Not measured | Only GPT-6 Sol measured |
| LMArena Vision: homework | 1286 | Not measured | Only GPT-6 Sol measured |
| Neither model has a result on the other 6 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-6 Sol | Muse Spark Open | Leader |
|---|---|---|---|
| OTIS Mock AIME | 100.0% | Not measured | Only GPT-6 Sol measured |
| FrontierMath, tiers 1 to 3 | 89.8% | Not measured | Only GPT-6 Sol measured |
| FrontierMath, tier 4 | 90.0% | Not measured | Only GPT-6 Sol measured |
| ProofBench | 83.0% | Not measured | Only GPT-6 Sol measured |
| LMArena Text: math | 1407 | Not measured | Only GPT-6 Sol measured |
| LMArena Text: mathematical work | 1424 | Not measured | Only GPT-6 Sol measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-6 Sol | Muse Spark Open | Leader |
|---|---|---|---|
| MedCode (Vals AI) | 47.1% | Not measured | Only GPT-6 Sol measured |
| SimpleQA Verified | 60.7% | Not measured | Only GPT-6 Sol measured |
| LMArena Text: science | 1389 | Not measured | Only GPT-6 Sol measured |
| LMArena Text: medicine and healthcare | 1366 | Not measured | Only GPT-6 Sol measured |
| LMArena Text: legal and government | 1396 | Not measured | Only GPT-6 Sol measured |
| LMArena Text: business and finance | 1380 | Not measured | Only GPT-6 Sol measured |
| Neither model has a result on the other 11 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index21
| Benchmark | GPT-6 Sol | Muse Spark Open | Leader |
|---|---|---|---|
| Design Arena: websites | 1292 | 1311 | Muse Spark Open |
| Design Arena: UI components | 1292 | Not measured | Only GPT-6 Sol measured |
| Design Arena: game dev | 1313 | 1290 | GPT-6 Sol |
| Design Arena: data viz | 1269 | Not measured | Only GPT-6 Sol measured |
| Design Arena: 3D | 1322 | Not measured | Only GPT-6 Sol measured |
| Design Arena: all code categories | 1301 | Not measured | Only GPT-6 Sol measured |
| Design Arena: SVG | 1318 | Not measured | Only GPT-6 Sol measured |
| Design Arena: ASCII art | 1226 | Not measured | Only GPT-6 Sol measured |
| LiveBench design | 82.2% | 60.3% | GPT-6 Sol |
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-6 Sol | Muse Spark Open | Leader |
|---|---|---|---|
| LiveBench data analysis | 81.2% | 60.2% | GPT-6 Sol |
| Public Benefits Bench (Vals AI) | 56.6% | Not measured | Only GPT-6 Sol measured |
| SAGE (Vals AI) | 44.8% | Not measured | Only GPT-6 Sol measured |
| MedScribe (Vals AI) | 82.0% | Not measured | Only GPT-6 Sol measured |
| Long context reasoning (AA-LCR) | 83.7% | 78.3% | GPT-6 Sol |
| DTBench | 97.3% | Not measured | Only GPT-6 Sol measured |
| LMCA | 59.1% | Not measured | Only GPT-6 Sol measured |
| Furniture assembly | 58.3% | Not measured | Only GPT-6 Sol measured |
| LMArena Text | 1398 | Not measured | Only GPT-6 Sol measured |
| LMArena Text: instruction following | 1413 | Not measured | Only GPT-6 Sol measured |
| LMArena Text: creative writing | 1378 | Not measured | Only GPT-6 Sol measured |
| LMArena Vision | 1240 | Not measured | Only GPT-6 Sol measured |
| LMArena Text: English | 1396 | Not measured | Only GPT-6 Sol measured |
| LMArena Text: non-English | 1388 | Not measured | Only GPT-6 Sol measured |
| LMArena Text: Chinese | 1412 | Not measured | Only GPT-6 Sol measured |
| LMArena Text: German | 1379 | Not measured | Only GPT-6 Sol measured |
| LMArena Text: Japanese | 1377 | Not measured | Only GPT-6 Sol measured |
| LMArena Text: Korean | 1336 | Not measured | Only GPT-6 Sol measured |
| LMArena Text: French | 1399 | Not measured | Only GPT-6 Sol measured |
| LMArena Text: Spanish | 1387 | Not measured | Only GPT-6 Sol measured |
| LMArena Text: Russian | 1402 | Not measured | Only GPT-6 Sol measured |
| LMArena Text: Polish | 1372 | Not measured | Only GPT-6 Sol measured |
| LMArena Text: multi-turn | 1414 | Not measured | Only GPT-6 Sol measured |
| LMArena Text: longer queries | 1416 | Not measured | Only GPT-6 Sol measured |
| LMArena Text: writing and language | 1389 | Not measured | Only GPT-6 Sol measured |
| LMArena Text: entertainment and media | 1373 | Not measured | Only GPT-6 Sol measured |
| LMArena Vision: English | 1229 | Not measured | Only GPT-6 Sol measured |
| LMArena Vision: reading text | 1258 | Not measured | Only GPT-6 Sol measured |
| LMArena Vision: creative writing | 1219 | Not measured | Only GPT-6 Sol measured |
| Neither model has a result on the other 17 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-6 Sol | Muse Spark Open | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Muse Spark Open costs less
Repository review
50K input + 3K output tokens
Muse Spark Open costs less
Agent loop
200K input + 10K output tokens
Muse Spark Open costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | OpenAI GPT-6 Sol | Meta Muse Spark Open |
|---|---|---|
| Maker | OpenAI | Meta |
| Weights | No public weights listed | Open weights |
| Released | September 22, 2026 | October 15, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $2.00 | $1.25 |
| Output $/Mtok | $10.00 | $4.25 |
| Coding index | 82.8 | 62.1 |
| Independent measurements behind it | 2 | 2 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-6 Sol better than Muse Spark Open for coding?
Pick GPT-6 Sol for coding: it scores 82.8 on the Vibe Coding Index against 62.1 for Muse Spark Open. Pick Muse Spark Open when output tokens cost 2.4x less, or when what matters is SciCode or LiveBench research, where it leads.
Which is cheaper, GPT-6 Sol or Muse Spark Open?
GPT-6 Sol costs $2.00 per million input tokens and $10.00 per million output tokens; Muse Spark Open costs $1.25 and $4.25. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-6 Sol or Muse Spark Open?
GPT-6 Sol, with 1.05M tokens against 1.05M.
How many benchmarks is the GPT-6 Sol vs Muse Spark Open comparison based on?
25 benchmarks have a result for both models as of October 21, 2026. GPT-6 Sol's coding index rests on 2 independent benchmarks and Muse Spark Open's on 2. Where only one model has a result, the page says so instead of naming a winner.